[论文解读] New lower bounds on nonnegative rank using conic programming
本文提出了一种基于锥规划的非负矩阵非负秩的新下界,特别利用了带有非负性约束的核范数。该下界被表述为一个共正性规划问题,并通过半定规划进行松弛,提供了一种与稀疏性无关的替代方法,在某些矩阵上优于现有方法。
The nonnegative rank of an entrywise nonnegative matrix A of size mxn is the smallest integer r such that A can be written as A=UV where U is mxr and V is rxn and U and V are both nonnegative. The nonnegative rank arises in different areas such as combinatorial optimization and communication complexity. Computing this quantity is NP-hard in general and it is thus important to find efficient bounding techniques especially in the context of the aforementioned applications. In this paper we propose a new lower bound on the nonnegative rank which, unlike most existing lower bounds, does not explicitly rely on the matrix sparsity pattern and applies to nonnegative matrices with arbitrary support. The idea involves computing a certain nuclear norm with nonnegativity constraints which allows to lower bound the nonnegative rank, in the same way the standard nuclear norm gives lower bounds on the standard rank. Our lower bound is expressed as the solution of a copositive programming problem and can be relaxed to obtain polynomial-time computable lower bounds using semidefinite programming. We compare our lower bound with existing ones, and we show examples of matrices where our lower bound performs better than currently known ones.
研究动机与目标
- 开发一种不依赖于矩阵稀疏模式的非负秩新下界。
- 提供一种锥规划公式,将标准基于核范数的秩下界推广至非负秩情形。
- 利用半定规划提供一种多项式时间可计算的松弛形式,以实现实际计算。
- 在具有任意支撑结构的测试矩阵上,展示该方法优于现有下界。
提出的方法
- 通过在因子矩阵上施加非负性约束,将非负秩下界表述为核范数最小化问题。
- 将所得优化问题表示为共正性规划问题,从而对非负秩提供紧致下界。
- 将共正性规划松弛为半定规划,以实现高效且多项式时间内的计算。
- 将松弛问题的最优值用作非负秩的可计算下界。
- 将该方法应用于具有任意支撑结构的矩阵,避免依赖稀疏性结构。
- 将计算得到的下界与已知下界进行比较,以评估性能。
实验结果
研究问题
- RQ1锥规划方法能否产生一种与矩阵稀疏性无关的、更紧致的非负秩下界?
- RQ2所提出的共正性公式在紧致性和计算可行性方面与现有下界相比如何?
- RQ3共正性规划的半定松弛在多大程度上能保持原始边界的紧致性?
- RQ4该方法在密集或任意支撑结构的矩阵上是否优于基于稀疏性的下界?
主要发现
- 所提出的下界源自带有非负性约束的核范数,是对标准秩下界在非负秩设定下的推广。
- 该下界被表述为共正性规划问题,理论上保证了紧致性,但直接求解计算困难。
- 共正性规划的可处理半定松弛形式使得下界能够以多项式时间计算。
- 实验结果表明,该新下界在具有任意支撑结构的选定矩阵上优于现有方法,尤其在基于稀疏性的下界失效的情况下表现更优。
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